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Abstract
The paper considers parameter estimation in count data models using penalized likelihood methods. The motivating data consists of multiple independent count variables with a moderate sample size per variable. The data were collected during the assessment of oral reading fluency (ORF) in school-aged children. A sample of fourth-grade students were given one of ten available passages to read with these differing in length and difficulty. The observed number of words read incorrectly (WRI) is used to measure ORF. Three models are considered for WRI scores, namely the binomial, the zero-inflated binomial, and the beta-binomial. We aim to efficiently estimate passage difficulty, a quantity expressed as a function of the underlying model parameters. Two types of penalty functions are considered for penalized likelihood with respective goals of shrinking parameter estimates closer to zero or closer to one another. A simulation study evaluates the efficacy of the shrinkage estimates using Mean Square Error (MSE) as metric. Big reductions in MSE relative to unpenalized maximum likelihood are observed. The paper concludes with an analysis of the motivating ORF data.
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Affiliation(s)
- Minh Thu Bui
- Department of Mathematics, Texas Christian University, Fort Worth, TX, USA
| | - Cornelis J. Potgieter
- Department of Mathematics, Texas Christian University, Fort Worth, TX, USA
- Department of Statistics, University of Johannesburg, Johannesburg, South Africa
| | - Akihito Kamata
- Simmons School of Education, Southern Methodist University, Dallas, TX, USA
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Weiss-Lehman CP, Werner CM, Bowler CH, Hallett LM, Mayfield MM, Godoy O, Aoyama L, Barabás G, Chu C, Ladouceur E, Larios L, Shoemaker LG. Disentangling key species interactions in diverse and heterogeneous communities: A Bayesian sparse modelling approach. Ecol Lett 2022; 25:1263-1276. [PMID: 35106910 PMCID: PMC9543015 DOI: 10.1111/ele.13977] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 12/07/2021] [Accepted: 01/02/2022] [Indexed: 11/30/2022]
Abstract
Modelling species interactions in diverse communities traditionally requires a prohibitively large number of species‐interaction coefficients, especially when considering environmental dependence of parameters. We implemented Bayesian variable selection via sparsity‐inducing priors on non‐linear species abundance models to determine which species interactions should be retained and which can be represented as an average heterospecific interaction term, reducing the number of model parameters. We evaluated model performance using simulated communities, computing out‐of‐sample predictive accuracy and parameter recovery across different input sample sizes. We applied our method to a diverse empirical community, allowing us to disentangle the direct role of environmental gradients on species’ intrinsic growth rates from indirect effects via competitive interactions. We also identified a few neighbouring species from the diverse community that had non‐generic interactions with our focal species. This sparse modelling approach facilitates exploration of species interactions in diverse communities while maintaining a manageable number of parameters.
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Affiliation(s)
| | - Chhaya M Werner
- Botany Department, University of Wyoming, Laramie, Wyoming, USA
| | - Catherine H Bowler
- School of Biological Sciences, University of Queensland, Brisbane, Queensland, Australia
| | - Lauren M Hallett
- Biology Department, University of Oregon, Eugene, Oregon, USA.,Environmental Studies Program, University of Oregon, Eugene, Oregon, USA
| | - Margaret M Mayfield
- School of Biological Sciences, University of Queensland, Brisbane, Queensland, Australia
| | - Oscar Godoy
- Departamento de Biología, Instituto Universitario de Investigación Marina (INMAR), Universidad de Cádiz, Puerto Real, Spain
| | - Lina Aoyama
- Biology Department, University of Oregon, Eugene, Oregon, USA.,Environmental Studies Program, University of Oregon, Eugene, Oregon, USA
| | - György Barabás
- Division of Theoretical Biology, Department of IFM, Linköping University, Linköping, Sweden
| | - Chengjin Chu
- Department of Ecology, State Key Laboratory of Biocontrol and School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Emma Ladouceur
- German Centre for Integrative Biodiversity Research (iDiv) Leipzig-Halle-Jena, Leipzig, Germany.,Department of Physiological Diversity, Helmholtz Centre for Environmental Research -UFZ, Leipzig, Germany
| | - Loralee Larios
- Department of Botany and Plant Sciences, University of California Riverside, Riverside, California, USA
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